Planning and Scheduling Issues in Supply Chains. Stephan Kreipl SAP Germany AG Michael Pinedo New York University

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1 Planning and Scheduling Issues in Supply Chains Stephan Kreipl SAP Germany AG Michael Pinedo New York University

2 Agenda 1 Planning and Scheduling in a Supply Chain 2 Implementing APO at Carlsberg A/S Denmark 3 Discussion

3 1 Planning and Scheduling in a Supply Chain 2 Implementing APO at Carlsberg A/S Denmark 3 Discussion

4 Example of a global Supply Chain Supplier Plants DCs Customers Products Resources

5 Medium vs. Short Term Planning (1)

6 Medium vs. Short Term Planning (2) Medium Term Planning Global Optimization Maximum Profit Product Hierarchies Time Buckets (days, weeks, ) Decide Where to produce How much to produce How much to deliver How much capacities Short Term Planning Local Optimization Disaggregate global plan Time continuous (seconds) Decide When to produce On which resources to produce Optimize production sequence

7 Integration between Medium and Short Term Planning

8 Scheduling in Medium and Short Term Planning Medium Term Planning Respects Short Term planning orders as fixed Capacity reduction from Short Term Planning Orders Material flow Short Term Planning Respects medium term planned demands as due dates No capacity reduction from medium term planning orders Material flow Short Term Horizon Medium Term Horizon

9 1 Planning and Scheduling in a Supply Chain 2 Implementing APO at Carlsberg A/S Denmark 3 Discussion

10 Carlsberg A/S Denmark 5 th. largest beer brewer in the world Founded in : Carlsberg is producing and distributing Coca Cola products in Denmark ~ 300 beer products ~ 150 Coca Cola products

11 Business Objectives Decrease inventory costs Optimize sourcing decisions Increase customer service level Change business into a more demand driven process

12 Carlsberg Supply Chain in Denmark

13 Solution Approach (1) Use advanced safety stock considering Service level Replenishment lead time Lot size Forecast error Future Forecast Midterm optimization considering Capacity constraints of the filling lines Transportation lead times between locations Set-up times modelled as fixed resource consumption Production cost of the different filling lines (bottleneck) Storage costs in the different locations Transportation costs between locations Violation of safety stock Late and non-delivery costs

14 Solution Approach (2) Short-term optimization considering Detailed production durations Sequence dependent set-up times Due dates based on the results from the midterm optimization Deliver- / Outbound planning Transportation lead times between locations Storage costs in the different locations Transportation costs between locations Violation of safety stock Late and non-delivery costs Transport planning Cross product (!) Objective: full trucks

15 Medium Term Optimization MIP Program (3 MIPs) 309 number of products 3184 number of location products (1.run) Planning period 12 weeks the first 5 weeks in daily buckets number of variables (1.run) number of constraints (1.run) number of discrete variables (1.run) Using product decomposition (5% partition) Pre-Phase to calculate starting solution (andreas.sandner@sap.com) Pre-Phase to calculate product priority for the product decomposition Result quality Runtime 10h LP solves problem in less than 15 minutes optimal Solution quality: 1-3% > compared to relaxation solution

16 Medium Term Optimizer Architecture LiveCache GUI Reporting Checking Control Model Generator Core-Model Time Decomposition Product Decomposition Priority Decomposition Meta-Heuristics LP MIP Basic Optimizer

17 Medium Term User Interface

18 Short term Optimization Genetic Algorithm Optimized Period: 7days Only one operation per product Sequence dependent set-up times Due dates are the planned transports from the midterm optimization Up to three alternatives resources Number of operations: Runtime: 1min

19 Scheduling Optimizer Architecture LiveCache GUI Reporting Checking Control Model Generator Core Model Time Decomposition Bottleneck Meta-Heuristics Constraint Programming Genetic Algorithm Basic Optimizer Campaign Optimizer

20 Short Term Planning User Interface

21 1 Planning and Scheduling in a Supply Chain 2 Implementing APO at Carlsberg A/S Denmark 3 Discussion

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